def test_predict2():
from model.astgcn import ASTGCN
from model.model_config import get_backbones
import mxnet as mx
ctx = mx.cpu()
all_backbones = get_backbones('configurations/PEMS08.conf',
'data/PEMS08/distance.csv', ctx)
net = ASTGCN(12, all_backbones)
net.initialize(ctx=ctx)
test_w = nd.random_uniform(shape=(8, 170, 3, 12), ctx=ctx)
test_d = nd.random_uniform(shape=(8, 170, 3, 12), ctx=ctx)
test_r = nd.random_uniform(shape=(8, 170, 3, 36), ctx=ctx)
output = net([test_w, test_d, test_r])
assert output.shape == (8, 170, 12)
assert type(output.mean().asscalar()) == np.float32
def test_predict2():
from model.astgcn import ASTGCN
from model.model_config import get_backbones
import torch
device = torch.device('cpu')
all_backbones = get_backbones('configurations/PEMS08.conf',
'data/PEMS08/distance.csv', device)
net = ASTGCN(12, all_backbones, 170, 3, [[12, 12], [12, 12], [36, 12]],
device)
test_w = torch.randn(8, 170, 3, 12).to(device)
test_d = torch.randn(8, 170, 3, 12).to(device)
test_r = torch.randn(8, 170, 3, 36).to(device)
output = net([test_w, test_d, test_r])
assert output.shape == (8, 170, 12)
assert type(output.detach().numpy().mean()) == np.float32
# save Z-score mean and std
transformer_data = {}
for type_ in ['week', 'day', 'recent']:
transformer = all_data['transformer'][type_]
transformer_data[type_ + '_mean'] = transformer.mean_
transformer_data[type_ + '_std'] = transformer.scale_
np.savez_compressed(
os.path.join(params_path, 'transformer_data'),
**transformer_data
)
# loss function MSE
loss_function = gluon.loss.L2Loss()
# get model's structure
all_backbones = get_backbones(args.config, adj_filename, ctx)
net = model(num_for_predict, all_backbones)
net.initialize(ctx = ctx)
for val_w, val_d, val_r, val_t in val_loader:
net([val_w, val_d, val_r])
break
net.initialize(ctx = ctx, init = MyInit(), force_reinit = True)
# initialize a trainer to train model
trainer = gluon.Trainer(net.collect_params(), optimizer, {'learning_rate': learning_rate})
# initialize a SummaryWriter to write information into logs dir
sw = SummaryWriter(logdir = params_path, flush_secs = 5)
# compute validation loss before training
# save Z-score mean and std
# stats_data = {}
# for type_ in ['week', 'day', 'recent']:
# stats = all_data['stats'][type_]
# stats_data[type_ + '_mean'] = stats['mean']
# stats_data[type_ + '_std'] = stats['std']
#
# np.savez_compressed(
# os.path.join(params_path, 'stats_data'),
# **stats_data
# )
loss_function = torch.nn.MSELoss()
all_backbones = get_backbones(args.config, adj_filename, device)
# print(all_backbones[0][0]['cheb_polynomials'])
num_of_features = 3
num_of_timesteps = [[points_per_hour * num_of_weeks, points_per_hour],
[points_per_hour * num_of_days, points_per_hour],
[points_per_hour * num_of_hours, points_per_hour]]
net = model(num_for_predict, all_backbones, num_of_vertices,
num_of_features, num_of_timesteps, device) #建立神经网络
net = net.to(device)
# it is the same as net.to(device)
# i.e., to() for module is in place, which is different from tensor:to()在张量和模中的位置不同
optimizer = optim.Adam(
net.parameters(),
# -*- coding:utf-8 -*-
import mxnet as mx
from model.model_config import get_backbones
from mxnet import nd
from model.astgcn import ASTGCN
ctx = mx.cpu()
all_backbones = get_backbones('configurations/PEMS04.conf',
'data/PEMS04/distance.csv', ctx)
net = ASTGCN(12, all_backbones)
net.initialize(ctx=mx.cpu())
test_w = nd.random_uniform(shape=(16, 307, 3, 12))
test_d = nd.random_uniform(shape=(16, 307, 3, 12))
test_r = nd.random_uniform(shape=(16, 307, 3, 36))
print(net([test_w, test_d, test_r]).shape)